Blood-Count-Derived Inflammatory Markers as Predictors of Response to Biologics and Small-Molecule Inhibitors in Psoriasis: A Multicenter Study.

: Psoriasis is an immune-mediated chronic disorder associated with various comorbidities. Even though biologics and small-molecule inhibitors are the mainstay treatment for moderate-to-severe psoriasis, there is no current consensus regarding which agent should be used for a specific type of patient. This paper aims to test the reliability of blood-count-derived inflammatory markers in assessing treatment response to biologics and small-molecule inhibitors in psoriasis. : Bio-naïve adult patients diagnosed with chronic plaque psoriasis fulfilling the inclusion criteria were enrolled. They were divided into study subgroups based on treatment of choice, and blood-count-derived inflammatory markers were analyzed at baseline, three-month, six-month, and at twelve-month visits. : A total of 240 patients were included. The highest number of patients underwent treatment with ixekizumab. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), platelet-to-monocyte ratio (PMR), monocyte-to-lymphocyte ratio (MLR), derived neutrophil-to-lymphocyte ratio (d-NLR), systemic inflammation response index (SIRI), systemic immune inflammation index (SII), and aggregate index of systemic inflammation (AISI) all varied significantly ( < 0.005) between the four visits. The psoriasis area severity index (PASI) score correlated with PLR, d-NLR, and SII, while the psoriasis scalp severity index (PSSI) score correlated with AISI and SIRI. More than half of patients reached the target goal of PASI90 at the six-month visit. A total of 77 patients were super-responders, with the highest number undergoing treatment with ixekizumab. Higher baseline values of d-NLR and SIRI are independent predictors of the super-responder status. : Blood-count-derived inflammatory markers can serve as indicators of treatment response to biologics in psoriasis, while d-NLR and SIRI were independent predictors of super-responders in our study.